NeighborNet: improved algorithms and implementation

David Bryant; Daniel H Huson

doi:10.3389/fbinf.2023.1178600

NeighborNet: improved algorithms and implementation

Front Bioinform. 2023 Sep 20:3:1178600. doi: 10.3389/fbinf.2023.1178600. eCollection 2023.

Authors

David Bryant^#¹, Daniel H Huson^#^{2

3}

Affiliations

¹ Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.
² Algorithms in Bioinformatics, University of Tübingen, Tübingen, Germany.
³ Cluster of Excellence: Controlling Microbes to Fight Infection, University of Tübingen, Tübingen, Germany.

^# Contributed equally.

Abstract

NeighborNet constructs phylogenetic networks to visualize distance data. It is a popular method used in a wide range of applications. While several studies have investigated its mathematical features, here we focus on computational aspects. The algorithm operates in three steps. We present a new simplified formulation of the first step, which aims at computing a circular ordering. We provide the first technical description of the second step, the estimation of split weights. We review the third step by constructing and drawing the network. Finally, we discuss how the networks might best be interpreted, review related approaches, and present some open questions.

Keywords: NeighborNet; SplitsTree; phylogenetic networks; planar graph drawing; split networks.

Grants and funding

The authors thank the Royal Society Te Apārangi (New Zealand) for funding under the Catalyst Leader program (Agreement # ILF-UOC1901). They acknowledge support by the Open Access Publishing Fund of the University of Tübingen.